One of the first studies to assess the impacts of climate projections and uncertainty in flood flows in the UK was by Cameron et al. (2000). They showed that the shift in estimates of the 0.01 annual exceedance probability flood (T = 100, see Chapter 11) might
Table 19.1 Examples of possible impacts of climate change due to changes in extreme weather and climate events, based on projections to the mid- to late twenty-first century. These do not take into account any changes or developments in adaptive capacity. The likelihood estimates in column 2 relate to the phenomena listed in column I. (IPCC, 2007a, Table 3.2.)
Over most land areas, warmer and fewer cold days and nights, warmer and more frequent hot days and nights Warm spells/heat waves. Frequency increases over most land areas
Heavy precipitation events. Frequency increase over most areas
Likelihood of future trends based on projections for the twenty-first century using SRES scenarios
Examples of major projected impacts by sector
Agriculture, forestry and ecosystems
Areas affected by drought Likely increases
Intense tropical cyclone Likely activity increases
Increased incidence of Likely^ extreme high sea level (excludes tsunaims)0
Increased yields in colder environments; decreased yields in warmer environments; increased insect outbreaks Reduced yields in warmer regions due to heat stress; increased danger of wildfire
Damage to crops; soil erosion, inability to cultivate land due to waterlogging of soils
Land degradation; lower yields/crop damage and failure; increased livestock deaths; increased risk of wildfire Damage to crops; windthrow (uprooting) of trees; damage to coral reefs
Salinisation of irrigation water, estuaries and freshwater systems
Effects on water resources relying on snowmelt; effects on some water supplies
Increased water demand; water-quality problems, e.g. algal blooms
Adverse effects on quality of surface and groundwater; contamination of water supply; water scarcity may be relieved
More widespread water stress
Power outages causing disruption of public water supply
Decreased freshwater availability due to saltwater intrusion
Reduced human mortality from decreased cold exposure
Increased risk of heat-related mortality, especially for the elderly, chronically sick, very young and socially isolated Increased risk of deaths, injuries and infectious, respiratory and skin diseases
Increased risk of food and water shortage; increased risk of malnutrition; increased risk of water- and food-borne diseases Increased risk of deaths, injuries, water- and food-borne diseases; post-traumatic stress disorders
Increased risk of deaths and injuries by drowning in floods; migration-related health effects
Industry, settlement and society
Reduced energy demand for heating; increased demand for cooling; declining air quality in cities; reduced disruption to transport due to snow and ice; effects on winter tourism Reduction in quality of life for people in warm areas without appropriate housing; impacts on the elderly, very young and poor
Disruption of settlements, commerce, transport and societies due to flooding; pressures on urban and rural infrastructures; loss of property
Water shortage for settlements, industry and societies; reduced hydropower generation potentials; potential for population migration
Disruption by flood and high winds; withdrawal of risk coverage in vulnerable areas by private insurers; potential for population migrations; loss of property
Costs of coastal protection versus costs of land-use relocation; potential for movement of populations and infrastructure; also see tropical cyclones above
"See Intergovernmental Panel on Climate Change (2007b) Table 3.7 for further details regarding definitions. 'Warming of the most extreme days and nights each year.
c Extreme high sea level depends on average sea level and on regional weather systems. It is defined as the highest I % of hourly values of observed sea level at a station for a given reference period.
In all scenarios, the projected global average sea level at 2100 is higher than in the reference period. The effect of changes in regional weather systems on sea level extremes has not been assessed.
UKCP09 predicted change (%) in winter precipitation in 2050 at 10%, 50% and 90% probability levels
be small relative to the uncertainty in estimating the correct magnitude under current conditions. The risk of over-topping a flood defence might, however, be reduced significantly. More recently, outputs from the Hadley Centre RCM (HadRM3) generated on a ~25-km cell-size grid were used by Kay et al. (2006) to investigate the impact in the 2080s of one climate change scenario on flood flows for 15 catchments in Great Britain (Fig.19.3). The study, like most impact studies in hydrology, used time series outputs of the relevant climate model variables (e.g. grid cell average rainfall, temperature, wind speed) to drive a calibrated rainfall-runoff model. This 'continuous simulation' approach is necessary to account for the variation in the forcing variables that occurs at different temporal scales. For example, it allows the soil moisture state in the hydrological model to vary in response to both seasonal changes in water balance and also changing conditions within a sequence of rain storms. Annual average rainfall increased over all but one of the catchments and eight showed an increase in flood frequency at most return periods. However, modelled peak river flows of a given return period were found to decrease for a number of the catchments in the south and east of England, despite an increase in winter mean and extreme rainfall because of increased summer and autumn soil moisture deficits, related to temperature change. Other catchments, further north or west, showed increased peak flows, with changes of over 50 per cent at the 50-year return period in some cases.
An important point to note from this analysis is that the impacts of climate change scenarios at a catchment level may be quite variable geographically. Care needs to be taken when interpreting these results, as they are based on a single RCM experiment (using driving data from one GCM under a single-emissions scenario). The results therefore represent just one 'snapshot' or realisation of many plausible future scenarios. Other emissions scenarios and GCM or RCM simulations may give quite different results, and ensemble runs would ideally be required to understand the sampling uncertainty.
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